We develop a robust computational framework to infer the motion of a flexible cylinder undergoing vortex induced vibration. Given scattered data in space–time on displacement and hydrodynamic forces, we use physics-informed neural networks (PINNs) (Raissi et al., 2019) to infer the entire motion and characterize the induced vibration by accurately estimating the structural parameters. The developed framework has the flexibility to simultaneously analyze the data over different space–time sub-domains and hence to yield more accurate estimations. We examine the efficiency of the formulation by estimating the damping coefficient of the flexible cylinder, where the training data is obtained by forward simulation of the coupled fluid–solid equations.

我们开发了一个强大的计算框架来推断一个柔性圆柱体的运动经历涡激振动。鉴于位移和水动力的时空分散数据，我们使用物理信息神经网络 (PINNs)（Raissi 等人，2019 年）通过准确估计结构参数来推断整个运动并表征诱导振动。开发的框架可以灵活地同时分析不同时空子域上的数据，从而产生更准确的估计。我们通过估计柔性圆柱体的阻尼系数来检查公式的效率，其中训练数据是通过耦合流固方程的正向模拟获得的。